{ "cells": [ { "cell_type": "markdown", "id": "269c4188", "metadata": {}, "source": [ "# Exercise sheet\n", "\n", "Some general remarks about the exercises:\n", "* For your convenience functions from the lecture are included below. Feel free to reuse them without copying to the exercise solution box.\n", "* For each part of the exercise a solution box has been added, but you may insert additional boxes. Do not hesitate to add Markdown boxes for textual or LaTeX answers (via `Cell > Cell Type > Markdown`). But make sure to replace any part that says `YOUR CODE HERE` or `YOUR ANSWER HERE` and remove the `raise NotImplementedError()`.\n", "* Please make your code readable by humans (and not just by the Python interpreter): choose informative function and variable names and use consistent formatting. Feel free to check the [PEP 8 Style Guide for Python](https://www.python.org/dev/peps/pep-0008/) for the widely adopted coding conventions or [this guide for explanation](https://realpython.com/python-pep8/).\n", "* Make sure that the full notebook runs without errors before submitting your work. This you can do by selecting `Kernel > Restart & Run All` in the jupyter menu.\n", "* For some exercises test cases have been provided in a separate cell in the form of `assert` statements. When run, a successful test will give no output, whereas a failed test will display an error message.\n", "* Each sheet has 100 points worth of exercises. Note that only the grades of sheets number 2, 4, 6, 8 count towards the course examination. Submitting sheets 1, 3, 5, 7 & 9 is voluntary and their grades are just for feedback.\n", "\n", "Please fill in your name here:" ] }, { "cell_type": "code", "execution_count": 2, "id": "220d541e", "metadata": {}, "outputs": [], "source": [ "NAME = \"Kees van Kempen\"\n", "NAMES_OF_COLLABORATORS = \"\"" ] }, { "cell_type": "markdown", "id": "b6944e4c", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "id": "c53fbab6", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "da0f2845f08ee29eb0450f8eff343e98", "grade": false, "grade_id": "cell-3cb26b1434512d8d", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**Exercise sheet 8**\n", "\n", "Code from the lectures:" ] }, { "cell_type": "code", "execution_count": 3, "id": "5e4391a6", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "1814f5ba5f2d71b14a4c534cfe3ad7ff", "grade": false, "grade_id": "cell-40c62687f6a2c579", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "import numpy as np\n", "rng = np.random.default_rng() \n", "import matplotlib.pylab as plt\n", "%matplotlib inline\n", "\n", "def fan_triangulation(n):\n", " '''Generates a fan-shaped triangulation of even size n.'''\n", " return np.array([[(i-3)%(3*n),i+5,i+4,(i+6)%(3*n),i+2,i+1] \n", " for i in range(0,3*n,6)],dtype=np.int32).flatten()\n", "\n", "def is_fpf_involution(adj):\n", " '''Test whether adj defines a fixed-point free involution.'''\n", " for x, a in enumerate(adj):\n", " if a < 0 or a >= len(adj) or x == a or adj[a] != x:\n", " return False\n", " return True\n", "\n", "from collections import deque \n", "\n", "def triangle_neighbours(adj,i):\n", " '''Return the indices of the three neighboring triangles.'''\n", " return [j//3 for j in adj[3*i:3*i+3]]\n", "\n", "def connected_components(adj):\n", " '''Calculate the number of connected components of the triangulation.'''\n", " n = len(adj)//3 # the number of triangles\n", " # array storing the component index of each triangle\n", " component = np.full(n,-1,dtype=np.int32) \n", " index = 0\n", " for i in range(n):\n", " if component[i] == -1: # new component found, let us explore it\n", " component[i] = index\n", " queue = deque([i]) # use an exploration queue for breadth-first search\n", " while queue:\n", " for nbr in triangle_neighbours(adj,queue.pop()):\n", " # the neighboring triangle has not been explored yet\n", " if component[nbr] == -1: \n", " component[nbr] = index\n", " queue.appendleft(nbr) # add it to the exploration queue\n", " index += 1\n", " return index\n", "\n", "def next_around_triangle(i):\n", " '''Return the label of the side following side i in counter-clockwise direction.'''\n", " return i - i%3 + (i+1)%3\n", "\n", "def prev_around_triangle(i):\n", " '''Return the label of the side preceding side i in counter-clockwise direction.'''\n", " return i - i%3 + (i-1)%3\n", "\n", "def vertex_list(adj):\n", " '''\n", " Return the number of vertices and an array `vertex` of the same size \n", " as `adj`, such that `vertex[i]` is the index of the vertex at the \n", " start (in ccw order) of the side labeled `i`.\n", " '''\n", " # a side i that have not been visited yet has vertex[i]==-1\n", " vertex = np.full(len(adj),-1,dtype=np.int32) \n", " vert_index = 0 \n", " for i in range(len(adj)):\n", " if vertex[i] == -1:\n", " side = i\n", " while vertex[side] == -1: # find all sides that share the same vertex\n", " vertex[side] = vert_index\n", " side = next_around_triangle(adj[side])\n", " vert_index += 1\n", " return vert_index, vertex\n", "\n", "def number_of_vertices(adj):\n", " '''Calculate the number of vertices in the triangulation.'''\n", " return vertex_list(adj)[0]\n", "\n", "def is_sphere_triangulation(adj):\n", " '''Test whether adj defines a triangulation of the 2-sphere.'''\n", " if not is_fpf_involution(adj) or connected_components(adj) != 1:\n", " return False\n", " num_vert = number_of_vertices(adj)\n", " num_face = len(adj)//3\n", " num_edge = len(adj)//2\n", " # verify Euler's formula for the sphere\n", " return num_vert - num_edge + num_face == 2\n", "\n", "def flip_edge(adj,i):\n", " if adj[i] == next_around_triangle(i) or adj[i] == prev_around_triangle(i):\n", " # flipping an edge that is adjacent to the same triangle on both sides makes no sense\n", " return False\n", " j = prev_around_triangle(i)\n", " k = adj[i]\n", " l = prev_around_triangle(k)\n", " n = adj[l]\n", " adj[i] = n # it is important that we first update\n", " adj[n] = i # these adjacencies, before determining m,\n", " m = adj[j] # to treat the case j == n appropriately\n", " adj[k] = m\n", " adj[m] = k\n", " adj[j] = l\n", " adj[l] = j\n", " return True\n", "\n", "def random_flip(adj):\n", " random_side = rng.integers(0,len(adj))\n", " return flip_edge(adj,random_side)\n", "\n", "import networkx as nx\n", "from mpl_toolkits.mplot3d import Axes3D\n", "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n", "\n", "def triangulation_edges(triangulation,vertex):\n", " '''Return a list of vertex-id pairs corresponding to the edges in the triangulation.'''\n", " return [(vertex[i],vertex[j]) for i,j in enumerate(triangulation) if i < j]\n", "\n", "def triangulation_triangles(triangulation,vertex):\n", " '''Return a list of vertex-id triples corresponding to the triangles in the triangulation.'''\n", " return [vertex[i:i+3] for i in range(0,len(triangulation),3)]\n", "\n", "def plot_triangulation_3d(adj):\n", " '''Display an attempt at embedding the triangulation in 3d.'''\n", " num_vert, vertex = vertex_list(adj)\n", " edges = triangulation_edges(adj,vertex)\n", " triangles = triangulation_triangles(adj,vertex)\n", " # use the networkX 3d graph layout algorithm to find positions for the vertices\n", " pos = np.array(list(nx.spring_layout(nx.Graph(edges),dim=3).values()))\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111, projection='3d')\n", " tris = Poly3DCollection(pos[triangles])\n", " tris.set_edgecolor('k')\n", " ax.add_collection3d(tris)\n", " ax.set_xlim3d(np.amin(pos[:,0]),np.amax(pos[:,0]))\n", " ax.set_ylim3d(np.amin(pos[:,1]),np.amax(pos[:,1]))\n", " ax.set_zlim3d(np.amin(pos[:,2]),np.amax(pos[:,2]))\n", " plt.show()\n", " \n", "def vertex_neighbors_list(adj):\n", " '''Return a list `neighbors` such that `neighbors[v]` is a list of neighbors of the vertex v.'''\n", " num_vertices, vertex = vertex_list(adj)\n", " neighbors = [[] for _ in range(num_vertices)]\n", " for i,j in enumerate(adj):\n", " neighbors[vertex[i]].append(vertex[j])\n", " return neighbors\n", "\n", "def vertex_distance_profile(adj,max_distance=30):\n", " '''Return array `profile` of size `max_distance` such that `profile[r]` is the number\n", " of vertices that have distance r to a randomly chosen initial vertex.'''\n", " profile = np.zeros((max_distance),dtype=np.int32)\n", " neighbors = vertex_neighbors_list(adj)\n", " num_vertices = len(neighbors)\n", " start = rng.integers(num_vertices) # random starting vertex\n", " distance = np.full(num_vertices,-1,dtype=np.int32) # array tracking the known distances (-1 is unknown)\n", " queue = deque([start]) # use an exploration queue for the breadth-first search\n", " distance[start] = 0\n", " profile[0] = 1 # of course there is exactly 1 vertex at distance 0\n", " while queue:\n", " current = queue.pop()\n", " d = distance[current] + 1 # every unexplored neighbour will have this distance\n", " if d >= max_distance:\n", " break\n", " for nbr in neighbors[current]:\n", " if distance[nbr] == -1: # this neighboring vertex has not been explored yet\n", " distance[nbr] = d\n", " profile[d] += 1\n", " queue.appendleft(nbr) # add it to the exploration queue\n", " return profile\n", " \n", "def perform_sweeps(adj,t):\n", " '''Perform t sweeps of flip moves, where 1 sweep is N moves.'''\n", " for _ in range(len(adj)*t//3):\n", " random_flip(adj)\n", "\n", "def batch_estimate(data,observable,k):\n", " '''Devide data into k batches and apply the function observable to each.\n", " Returns the mean and standard error.'''\n", " batches = np.reshape(data,(k,-1))\n", " values = np.apply_along_axis(observable, 1, batches)\n", " return np.mean(values), np.std(values)/np.sqrt(k-1)" ] }, { "cell_type": "markdown", "id": "bed55184", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "8c9a6c198119d4649dd87308e8933611", "grade": false, "grade_id": "cell-5f5adc7840fea9ad", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Estimating Hausdorff dimensions in various 2D quantum gravity models \n", "\n", "**(100 Points)**\n", "\n", "In the lecture we considered the model of two-dimensional Dynamical Triangulations of the 2-sphere. The corresponding partition function is\n", "$$ Z^{U}_{S^2,N} = \\sum_T 1, \\tag{1}$$\n", "where the sum is over all triangulations of size $N$ with the topology of $S^2$, each of which is represented as an adjacency list $\\operatorname{adj}: \\{0,\\ldots,3N-1\\} \\to \\{0,\\ldots,3N-1\\}$. To emphasize that we are dealing with the **uniform** probability distribution on such triangulations, we have added the label $^U$. It is a lattice model of two-dimensional Euclidean quantum gravity with no coupled matter.\n", "\n", "One can also consider two-dimensional quantum gravity coupled to matter fields (e.g. a scalar field) supported on the geometry. Formally the corresponding path integral in the continuum reads\n", "$$ Z = \\int [\\mathcal{D}g_{ab}]\\int [\\mathcal{D}\\phi] e^{-\\frac{1}{\\hbar}(S_E[g_{ab}] + S_m[\\phi,g_{ab}])} = \\int [\\mathcal{D}g_{ab}]e^{-\\frac{1}{\\hbar}S_E[g_{ab}]} Z^*_m[g_{ab}],$$\n", "where $S_m[\\phi,g_{ab}]$ and $Z_m[g_{ab}]$ are the matter action and path integral of the field $\\phi$ on the geometry described by $g_{ab}$. The natural analogue in Dynamical Triangulations is\n", "$$Z^*_{S^2,N} = \\sum_T Z^*_m[T],$$\n", "where the sum is over the same triangulations as in (1) but now the summand $Z^*_m[T]$ is the lattice partition function of a matter system supported on the triangulation $T$, which generically depends in a non-trivial way on $T$. For instance, the matter system could be an Ising model in which the spin are supported on the triangles of $T$ and $Z^{\\text{Ising}}_m[T]$ would be the corresponding Ising partition function.\n", "In other words, when Dynamical Triangulations are coupled to matter the uniform distribution $\\pi^U(T) = 1/Z^U_{S^2,N}$ is changed into a non-uniform distribution $\\pi^*(T) = Z^*_m[T] / Z^*_{S^2,N}$. This can have significant effect on the critical exponents of the random triangulation as $N\\to\\infty$, like the Hausdorff dimension. \n", "\n", "The goal of this exercise is to estimate the **Hausdorff dimension** of random triangulations in four different models and to conclude based on this that they belong to four different universality classes (i.e. that if they possess well-defined continuum limits that they are described by four different EQFTs): \n", "* $Z^{U}_{S^2,N}$: the standard Dynamical Triangulations with **U**niform distribution (U)\n", "* $Z^{W}_{S^2,N}$: triangulations coupled to a matter system called a Schnyder **W**ood (W)\n", "* $Z^{S}_{S^2,N}$: triangulations coupled to a matter system called a **S**panning tree (S)\n", "* $Z^{B}_{S^2,N}$: triangulations coupled to a matter system called a **B**ipolar orientation (B)\n", "\n", "What these matter systems precisely represent will not be important. We have provided for you a **black box generator** that samples from the corresponding four distributions $\\pi^U(T)$, $\\pi^W(T)$, $\\pi^S(T)$, $\\pi^B(T)$. It does so in an efficient manner (linear time in $N$) using direct Monte Carlo sampling algorithms and therefore returns independent samples with exactly the desired distribution $\\pi^*(T)$ (within numerical precision).\n", "\n", "The black box generator is provided by the executable program `generator` provided to you on the science server. It can be called directly from this notebook with the following function `generate_random_triangulation`, that takes the desired size $N$ and model (`'U'`,`'W'`, `'S'`, `'B'`) and returns a single random triangulation in the usual form of an adjacency list." ] }, { "cell_type": "code", "execution_count": 4, "id": "bcc7acba", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "7d6abad00aa217998ca44ecc5e89f423", "grade": false, "grade_id": "cell-266ff66f880583d7", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import subprocess\n", "\n", "def generate_random_triangulation(n,model):\n", " '''\n", " Returns a random triangulation generated by the program `generator` in the form \n", " of an array of length 3n storing the adjacency information of the triangle sides.\n", " Parameters:\n", " n - number of triangles in the triangulation, must be positive and even\n", " model - a one-letter string specifying the model from which the triangulation is sampled:\n", " 'U': Uniform triangulations\n", " 'W': Schnyder-Wood-decorated triangulations\n", " 'S': Spanning-tree decorated triangulations\n", " 'B': Bipolar-oriented triangulations\n", " '''\n", " program = \"/vol/cursus/NM042B/bin/generator\"\n", " output = subprocess.check_output([program,\"-s{}\".format(n),\"-t{}\".format(model)]).decode('ascii').split('\\n')[:-1]\n", " return np.array([int(num) for num in output],dtype=np.int32)\n", "\n", "adj = generate_random_triangulation(100,'B')\n", "is_sphere_triangulation(adj)" ] }, { "cell_type": "markdown", "id": "4518f51f", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "37e92f3a59f2d5c6d117868d04d8f0d4", "grade": false, "grade_id": "cell-6aacf5fa6d8c4eb9", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Recall that the **distance profile** $\\rho_T(r)$ of a triangulation is defined as \n", "$$ \\rho_T(r) = \\frac{1}{V} \\sum_{x=0}^{V-1} \\sum_{y=0}^{V-1} \\mathbf{1}_{\\{d_T(x,y)=r\\}},$$\n", "where $V = (N+4)/2$ is the number of vertices and $d_T(x,y)$ is the graph distance between the vertices with label $x$ and $y$." ] }, { "cell_type": "markdown", "id": "d59143f0", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "afcdbf86f64bd17b8ac9b4f9ec422206", "grade": false, "grade_id": "cell-8e6d6fcefb5ab644", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**(a)** Let $T$ be a random triangulation of size $N$ and $X$, $Y$ two independent numbers chosen uniformly from $0,\\ldots,V-1$, corresponding to two random vertices in $T$. Explain with a calculation that $\\frac{1}{V}\\mathbb{E}[ \\rho_T(r) ] = \\mathbb{P}(d_T(X,Y) = r)$ and that the expected distance between $X$ and $Y$ is related to the distance profile via\n", "\n", "$$\n", "\\mathbb{E}[d_T(X,Y)] = \\frac{1}{V}\\sum_{r=0}^\\infty r\\, \\mathbb{E}[ \\rho_T(r) ]. \\tag{2}\n", "$$\n", "\n", "**(20 pts)**" ] }, { "cell_type": "markdown", "id": "dd1b43bf", "metadata": { "deletable": false, "nbgrader": { "cell_type": "markdown", "checksum": "74963ed3d7cbd9eaa06be2e66a8f939e", "grade": true, "grade_id": "cell-f86454063d193cd6", "locked": false, "points": 20, "schema_version": 3, "solution": true, "task": false } }, "source": [ "**To proof**\n", "\n", "$\\frac{1}{V}\\mathbb{E}[ \\rho_T(r) ] = \\mathbb{P}(d_T(X,Y) = r)$\n", "\n", "**Proof**\n", "\n", "$$\n", "\\frac{1}{V} \\mathbb{E}\\left[ \\rho_T(r)\\right]\n", " = \\frac{1}{V} \\mathbb{E} \\left[\\frac{1}{V} \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbf{1}_{\\{d_T(x,y)=r\\}} \\right]\n", " = \\frac{1}{V^2} \\mathbb{E} \\left[ \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbf{1}_{\\{d_T(x,y)=r\\}} \\right]\n", " = \\frac{1}{V^2} \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbb{E} \\left[ \\mathbf{1}_{\\{d_T(x,y)=r\\}} \\right]\n", "$$\n", "\n", "The order of summation is changed, as the sum of expectation values is equal to the expectation value of the sum.\n", "The latter expectation value of the indicator function is exactly equal to the chance $\\mathbb{P}(d_T(x,y)=r)$ for given $x, y$.\n", "For the uniformly distributed $X, Y$, we find $\\mathbb{P}(X = x) = \\frac{1}{V} = \\mathbb{P}(Y = y)$.\n", "This allows us to write the right hand side as follows.\n", "\n", "$$\n", "\\frac{1}{V} \\mathbb{E}\\left[ \\rho_T(r)\\right]\n", " = \\frac{1}{V^2} \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbb{P}(d_T(x,y)=r)\n", " = \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbb{P}(X = x) \\mathbb{P}(Y = y) \\mathbb{P}(d_T(x,y)=r)\n", " = \\mathbb{P}(d_T(X,Y)=r),\n", "$$\n", "\n", "which is what we sought.\n", "\n", "Using this result, it is just a matter of writing out the definition of an expectation value to get to the result.\n", "\n", "$$\n", "\\mathbb{E}[d_T(X,Y)] = \\sum_{r=0}^\\infty r\\, \\mathbb{P}(d_T(X,Y) = r) = \\frac{1}{V}\\sum_{r=0}^\\infty r\\, \\mathbb{E}[ \\rho_T(r) ].\n", "$$" ] }, { "cell_type": "markdown", "id": "29704f5d", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "e2cc0493d54bcf087ce14bcb2e8a8d2f", "grade": false, "grade_id": "cell-aafca9797e5cfee4", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**(b)** We will work under the assumption that \n", "\n", "$$\n", "\\mathbb{E}[\\rho_T(r)] \\approx V^{1-1/d_H} f(r V^{-1/d_H})\n", "$$ \n", "\n", "for a positive real number $d_H$ called the **Hausdorff dimension** and a continuous function $f$ that are both independent of $N$ but do depend on the model. Show that \n", "\n", "$$\n", "\\mathbb{E}[d_T(X,Y)] \\approx c\\,V^{1/d_H}, \\qquad c = \\int_0^\\infty \\mathrm{d}x\\,x\\,f(x). \\tag{3}\n", "$$\n", "\n", "_Hint:_ Approximate the summation by an integral. **(15 pts)**" ] }, { "cell_type": "markdown", "id": "0c062ba6", "metadata": { "deletable": false, "nbgrader": { "cell_type": "markdown", "checksum": "2db525e8acbc2412c1c5948052526a15", "grade": true, "grade_id": "cell-bcf3b38d64a4408d", "locked": false, "points": 15, "schema_version": 3, "solution": true, "task": false } }, "source": [ "**To proof**\n", "\n", "$$\n", "\\mathbb{E}[d_T(X,Y)] \\approx c\\,V^{1/d_H}, \\qquad c = \\int_0^\\infty \\mathrm{d}x\\,x\\,f(x)\n", "$$\n", "\n", "**Proof**\n", "\n", "$$\n", "\\mathbb{E} \\left[ d_T(X,Y) \\right]\n", " = \\frac{1}{V} \\sum_{r=0}^\\infty r\\, \\mathbb{E} \\left[ \\rho_T(r) \\right]\n", " = \\frac{1}{V} \\sum_{r=0}^\\infty rV^{1-1/d_H}f(rV^{-1/d_H})\n", " = \\frac{1}{V} \\sum_{r=0}^\\infty xV^{1/d_H} \\cdot V^{1-1/d_H}f(x)\n", " = \\sum_{r=0}^\\infty xf(x),\n", "$$\n", "where the first equality sign is due to (2), the second due to the given assumption, the third using $x = rV^{-1/d_H}$.\n", "\n", "Now we approximate the summation by an integral.\n", "\n", "$$\n", "\\sum_{r=0}^\\infty xf(x)\n", " \\approx \\int_{r=0}^\\infty xf(x)dr\n", " = V^{1/d_H} \\int_{x=0}^\\infty xf(x)dx\n", " = cV^{1/d_H},\n", "$$\n", "using $\\frac{dr}{dx} = V^{1/d_H}$ for substitution.\n", "This yields the desired approximation\n", "$$\n", " \\mathbb{E} \\left[ d_T(X,Y) \\right] \\approx cV^{1/d_H}.\n", "$$" ] }, { "cell_type": "markdown", "id": "eba53e6d", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "ba14acd8cc24c2dfea35f3b8106cdfc8", "grade": false, "grade_id": "cell-fcab32195688a5c5", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**(c)** For each of the four models estimate $\\mathbb{E}[d_T(X,Y)]$ with errors for $N = 2^7, 2^8, \\ldots, 2^{12}$ using (2) and based on $100$ samples each. Store your data in the file `qgdimension.hdf5`. Make an estimate of $d_H$ (with errors) for each of the models by fitting the parameters $c$ and $d_H$ of the ansatz (3). For each model, plot the data together with the fit in a log-log plot. **(40 pts)**" ] }, { "cell_type": "code", "execution_count": 12, "id": "ee683060", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "c3664034dec3a350f7fe0533fe2454cb", "grade": true, "grade_id": "cell-01f5fde55f35f2dc", "locked": false, "points": 15, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "models = ['U','W','S','B']\n", "sizes = [2**k for k in range(7,13)]\n", "num_vertices = (np.array(sizes)+4)/2\n", "measurements = 100\n", "\n", "# data gathering and storing in qgdimension.hdf5\n", "import h5py\n", "\n", "max_distance = 30\n", "def expected_distance(V, adj, max_distance=30):\n", " '''\n", " Calculates the expectation value of the distance profile given the amount\n", " of vertices V, an array of adjacencies for a triangulation sample,\n", " and max_distance as upper limit for the summation for the expectation value.\n", " '''\n", " return 1/V * vertex_distance_profile(adj,max_distance)@np.arange(max_distance)\n", "\n", "with h5py.File(\"qgdimension.hdf5\", \"a\") as f:\n", " if not \"num-vertices\" in f:\n", " f.create_dataset(\"num-vertices\",data=num_vertices)\n", " \n", " for model in models:\n", " models_key = f\"expectation-graph-distance-{model}\"\n", " if not models_key in f:\n", " graph_distance_expectations = np.zeros((len(num_vertices), measurements, max_distance))\n", " for idx_N, N in enumerate(num_vertices):\n", " V = (N + 4)/2\n", " for idx_measurement in range(measurements):\n", " adj = generate_random_triangulation(N, model)\n", " expectation = expected_distance(V, adj, max_distance)\n", " graph_distance_expectations[idx_N][idx_measurement] = expectation\n", "\n", " f.create_dataset(models_key,data=graph_distance_expectations)" ] }, { "cell_type": "code", "execution_count": 7, "id": "351f7a01", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "000725107fe51acebc0bc68eef8c1c9c", "grade": true, "grade_id": "cell-9e8f666073e1e2df", "locked": false, "points": 25, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Fitting and plotting\n", "from matplotlib import pyplot as plt\n", "from scipy.optimize import curve_fit\n", "\n", "# Define a dictionary of model names for the plot titles.\n", "model_names = {\"U\": \"Uniform triangulations\",\n", " \"W\": \"Schnyder-Wood-decorated triangulations\",\n", " \"S\": \"Spanning-tree decorated triangulations\",\n", " \"B\": \"Bipolar-oriented triangulations\"}\n", "\n", "d_H_list = {}\n", "\n", "with h5py.File(\"qgdimension.hdf5\", \"r\") as f:\n", " num_vertices = np.array(f[\"num-vertices\"])\n", " expectations = {model: np.array(f[f\"expectation-graph-distance-{model}\"]) for model in models}\n", " \n", " fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n", " axs = axs.ravel()\n", " fig.suptitle(r\"Graph distance expectation Monte Carlo simulations and Hausdorff dimension $d_H$ fits using $\\mathbb{E}[d_T(X,Y)] \\approx c\\,V^{1/d_H}$ for different triangulation models\")\n", " \n", " for idx_model, model in enumerate(models):\n", " # Calculate mean and standard deviation of the expectations.\n", " # TODO: Look at whether I store the right data and do the right calculations.\n", " mu = np.mean(expectations[model], 1)\n", " sigma = np.std(expectations[model], 1)\n", "\n", " fitfunc = lambda V, c, d_H: c*V**(1/d_H)\n", " popt, pcov = curve_fit(fitfunc, num_vertices, mu, sigma=sigma)\n", " d_H_list[model] = popt[1]\n", " num_vertices_fit = np.linspace(np.min(num_vertices)/2, np.max(num_vertices)*2, 1000)\n", "\n", " ax = axs[idx_model]\n", " ax.set_title(f\"{model_names[model]} ({model})\")\n", " ax.errorbar(num_vertices, mu, sigma, label=\"Monte Carlo\",\n", " fmt='.', markersize=10, capsize=4)\n", " ax.plot(num_vertices_fit, fitfunc(num_vertices_fit, *popt),\n", " label=r\"fit: $c = {:.2f}$, $d_H = {:.2f}$\".format(*popt))\n", " ax.set_xlabel(r\"$V$\")\n", " ax.set_ylabel(r\"$\\mathbb{E}[d_T(X,Y)]$\")\n", " ax.set_yscale(\"log\")\n", " ax.set_xscale(\"log\")\n", " ax.grid(True, which=\"both\", ls=\"-\")\n", " ax.legend()\n", " \n", " fig.tight_layout()\n", " fig.show()" ] }, { "cell_type": "markdown", "id": "b505b3cf", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "be7888d11d6b9ca0f2666739857578cb", "grade": false, "grade_id": "cell-032c7f8d6147d9f9", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**(d)** Produce a *collapse* plot for each of the four models as follows: plot \n", "$$V^{1/d_H}\\,\\mathbb{E}[\\frac{1}{V}\\rho_T(r)] \\quad\\text{ as function of } x = r / V^{1/d_H},$$ \n", "where for $d_H$ you take the estimate obtained in the previous exercise. Show errors in the mean distance profiles via shaded regions (just like in the lecture). Verify that the curves collapse reasonably well. **(25 pts)**" ] }, { "cell_type": "code", "execution_count": 11, "id": "988bfe95", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "7b7eceb7923231bc3710d4e3036265b6", "grade": true, "grade_id": "cell-faf328e7505cf6a2", "locked": false, "points": 25, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "equilibration_sweeps = 500\n", "measurement_sweeps = 2\n", "measurements = 200\n", "\n", "max_distance = 15\n", "\n", "for model in models:\n", " d_H = d_H_list[model]\n", " \n", " # Code mostly copied from lecture 8.\n", " mean_profiles = []\n", " for size in sizes:\n", " #adj = generate_random_triangulation(size, model)\n", " #perform_sweeps(adj,equilibration_sweeps)\n", " profiles = []\n", " for _ in range(measurements):\n", " #perform_sweeps(adj,measurement_sweeps)\n", " adj = generate_random_triangulation(size, model)\n", " profiles.append(vertex_distance_profile(adj,max_distance))\n", " mean_profiles.append([batch_estimate(data,np.mean,20) for data in np.transpose(profiles)])\n", "\n", " #for profile in mean_profiles:\n", " # plt.plot([y[0] for y in profile])\n", " #for profile in mean_profiles:\n", " # plt.fill_between(range(len(profile)),\n", " # [y[0]-y[1] for y in profile],[y[0]+y[1] for y in profile],alpha=0.2)\n", " #plt.legend(num_vertices, title=\"V\")\n", " #plt.xlabel(\"x\")\n", " #plt.ylabel(r\"$\\mathbb{E}[\\rho_T(r)]$\")\n", " #plt.title(\"Mean distance profile (errors shown as shaded regions)\")\n", " #plt.show()\n", "\n", " for i, profile in enumerate(mean_profiles):\n", " rvals = np.arange(len(profile))\n", " plt.plot(rvals/num_vertices[i]**(1/d_H),\n", " [y[0]*num_vertices[i]**(1/d_H - 1) for y in profile])\n", " for i, profile in enumerate(mean_profiles):\n", " plt.fill_between(np.arange(len(profile))/num_vertices[i]**(1/d_H),\n", " [(y[0]-y[1])*num_vertices[i]**(1/d_H - 1) for y in profile],\n", " [(y[0]+y[1])*num_vertices[i]**(1/d_H - 1) for y in profile],\n", " alpha=0.2)\n", " plt.legend(sizes, title=\"V\")\n", " plt.xlabel(r\"$x = r/V^{1/d_H}$\")\n", " plt.ylabel(r\"$V^{1/d_H}\\,\\mathbb{E}[\\frac{1}{V}\\rho_T(r)]$\")\n", " plt.xlim(0,3)\n", " plt.title(f\"Finite-size scaling with Hausdorff dimension $d_H = {d_H:.2f}$\")\n", " plt.show()\n", "\n" ] }, { "cell_type": "markdown", "id": "d8f25787", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "7f19410ed936f838773ee891b059d1a3", "grade": false, "grade_id": "cell-65ae9c46ece5b657", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**(e) Bonus exercise:** Make more robust estimates of $d_H$ by optimizing the quality of the collapse. You could do this (for each model separately) by taking $\\hat{f}(r) = \\mathbb{E}[\\rho_T(r)] / V_0$, where the right-hand side is the mean distance profile for the largest system size with $V_0 = (2^{12} + 4)/2$ vertices. Then according to our assumption, for another size $V \\leq V_0$ we expect $\\mathbb{E}[\\rho_T(r)] / V \\approx k \\hat{f}(kr)$, where $k \\geq 1$ is a scale factor that should be $k\\approx (V_0/V)^{1/d_H}$. Making sure to interpolate the function $\\hat{f}(r)$ (using [`scipy.interpolate.interp1d`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d)), this scale factor can be determined by fitting the curve $k \\hat{f}(kr)$ to the data $\\mathbb{E}[\\rho_T(r)] / V$. Then $d_H$ can be estimated by fitting $k$ versus $V$. **(20 bonus points, but note that maximum grade is 10)**" ] }, { "cell_type": "code", "execution_count": null, "id": "ed4424ce", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "199ffddc14c77d4174b92a61368cd5c9", "grade": true, "grade_id": "cell-e24b0602e4e8257d", "locked": false, "points": 20, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "# YOUR CODE HERE\n", "raise NotImplementedError()" ] }, { "cell_type": "code", "execution_count": null, "id": "c9e50c10", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }